Computer predictive models have been used for many years in a diverse number of areas, such as in the financial industry. However current methods have difficulty in providing an automated or semi-automated mechanism for determining whether a suspicious activity, such as credit card fraud, may have occurred. As an illustration, previous systems experience problems in generating fraud indicative scores because such systems generally store aggregated/derived data and not raw data, thereby losing relevant history associated with an entity to perform scoring. Moreover, aggregated/derived data is specifically suited for a particular application and purpose (e.g., a fraud scoring purpose), but lacks flexibility to readily be used by other types of scoring applications.